Tree aboveground biomass (e.g., bole, branches, and foliage), M, plays key roles in forest management as it is the basis for evaluating the sink and flux of, for example, carbon and nitrogen, stand productivity, dendro-energy, litter & root biomass, hydrological parameters, among others. With the aim of further simplifying and understanding M, the central objective of this research was to review available techniques to develop, test, and validate two independent novel non-destructive, semi-empirical models using four major M datasets: (i) the shape dimensional bio-physical, MSD; and (ii) the restrictive mathematical, MNR, models. The proposed models advance and test how each of both approaches: (i) constant or (ii) variable scalar coefficients perform when predicting M with major assumptions bearing bio-physical principles. Results showed that M has to be predicted eventually with variable scalar coefficients; both models predicted compatible M figures; the evaluations matched the conventional equation well; and the independent data sets were well validated; the coefficients of determination, r2, and the standard errors, Sx%, had values > 96% and <20%, respectively, for most tested conifer tree species. In spite of demonstrating empirically and physically the ontogenetic-dependency of scalar coefficients, the MNR model, with constant β-scalar and variable a-intercept coefficients, performed slightly better, and precision appeared to be a function of the tree species growing in different forest ecosystems. Therefore, better parameterization advances for the testing and validation of the MSD model that uses variable scalar coefficients, which are consistent with ontogenetic principles, are preliminarily recommended for M assessments. The updated revision of models, the independent development, the construction using different assumptions, the individual mathematical and bio-physical parameterization, the consistency on M assessments, and the bearing of physical and biological properties are key pieces of scientific information presented in this report are required in modern forest management when predicting M and associated variables and attributes.

Litter, LS, is the organic material in which locates in the top A soil horizon, playing key ecological roles in forests. Models, in contrast to common allocation factors, must be used in LS assessments as they are currently absent in the scientific literature. Its evaluation assess the mass, input and flux of several bio-geo-chemicals, rainfall interception as one component of the local hydrology, and wildfire regimes, among others, hence its importance in forestry. The aim of this study was to: (i) develop models to assess LS, accumulation, and loss rates; and (ii) assess rainfall interception and fire regimes in 133 northern forest plantations of Mexico. Two developed techniques: the statistical model (SMLS) and the mass balance budget model (MBMLS) tested and validated local and regional LS datasets. Models use basal area, timber, aboveground tree biomass, litter fall, accumulation, and loss sub-models. The best fitting model was used to predict rainfall interception and fire behavior in forest plantations. Results showed the SMLS model predicted and validated LS datasets (p = 0.0001; r2 = 0.82 and p = 0.0001; r2 = 0.79) better than the MBMLS model (p = 0.0001; r2 = 0.32 and p = 0.0001; r2 = 0.66) but the later followed well tendencies of Mexican and World datasets; counts for inputs, stocks, and losses from all processes and revealed decomposition loss may explain ≈40% of the total LS variance. SMLS predicted forest plantations growing in high productivity 40-year-old stands accumulate LS > 30 Mg ha−1 shifting to the new high-severity wildfire regime and intercepting ≈15% of the annual rainfall. SMLS is preliminarily recommended for LS assessments and predicts the need of LS management in forest plantations (>40-year-old) to reduce rainfall interception as well as the risk of high-severity wildfires. The novel, flexible, simple, contrasting and consistent modeling approaches is a piece of scientific information required in forest management.